Intelligence

What is intelligence? We know it as IQ. But not so many know what the “Q” is. Q stands for quotient. IQ is Intelligence Quotient. IQ was considered as a measure of intelligence of the person. Until other Qs kicked in. There are many of them: MQ PQ AQ BQ EQ DQ HQ FQ WQ SQ… Body intelligence, Health intelligence, Practical intelligence, Moral intelligence and so on, and so forth. I am sure they will run out of the letters of English alphabet, by labeling newly discovered/isolated intelligences.

It is possible to identify if some of those Qs are absent [due to damage or disease]. By augmenting the impaired humans with intelligent tools, we could compensate for some Qs deficit. The same and similar technologies & tools (especially mental) could be pushed to the limit, and used by all people – to help us all – to flourish in the second machine age. It looks to me that recreation of narrow intelligence is nothing else than the building of new tools, but not building the real [human or stronger] intelligence. It is extending ourselves, not replicating ourselves.

I like this definition [by Michio Kaku?] of Intelligence. Intelligence is our capabilities to firmly predict the future. I would re-phrase it to our capabilities to firmly predict the future & the past. Because the history is usually written by those in power, screwed and warped for the sake of their benefit. Hence the abilities to see/know the past & future – in high resolution – is the intelligence. We could compute it. The method doesn’t matter. The further and the more firmly we see the future, the more intelligent we are. The ultimate intelligence (as of today) would be trully seeing in high resolution the entire Light Cone of our world.

Humans & Intelligence

It seems like the humans are the most intelligent species out there, on our planet Earth. Maybe we are like small ants near the leg of the huge elephant, not seeing the elephant… But it’s OK to look to what we see, the less intelligent species. It is relevant to think about this into more details. What is a human? What is a minimum livable human? What makes the human intelligent?

This is strict. There are humans without limbs, because of injuries, diseases (including born). There are humans without organic heart, with electro-mechanical pump. There are humans without kidneys on dialysis. There are humans who could not see or hear. And it leads us to the human brain. As long as the brain is up and running – it makes a human the human.

I don’t know if it’s brain alone, or brain plus spinal cord. But it is clear enough, as long as the brain works as expected, we accept the human as a peer. The contrary is right too, the people with damaged brain, but with human body – we accept them as humans. But here we are talking about intelligent human, human intelligence. So the case with the brain is what we are interested in.

Human Brain

This is still difficult. On the one side, we know pretty well, what our brain is. On the other side, we don’t know deep/good enough what our brain is. It is even difficult to explain the size vs. intelligence. How the small brain of grey parrot could provide intelligence on par with much bigger brain of some chimps? Or how the smaller human brain produce bigger intelligence than 3x bigger elephant’s brain. This lead us to the thinking about form/structure vs. function. Probably the structure is more in charge of intelligence than the size?

Very interesting hypo about the wiring pattern is called cliques & cavities. The function could be possibly encoded into directed graphs of neurons. The connections could be unidirectional, bidirectional. And they could compute [locally] something relevant, and interop with other cliques at the higher level. The cliques could encode/process something like 11-dimensional “things”. Who wants to check out whether recent Hinton’s capsules are similar to those cliques?

The #1 problem is the absence of brain scanners, that could scan the brain deeply and densely enough, without damaging the brain. If we could have the brain scans [electricity, chemistry] at all depth levels, down to the millisecond, it would help a lot. Resolution down to the nanosecond would be even better… But we don’t have such scanners yet. Some scanning technologies damage the brain. Others are not hires enough. Maybe Paul Allen’s Brain Institute could invent something any time soon.

Enlightenment

20 years ago something bright was discovered in the rats brain. The light was produced by the rats brain. Since that time, there is still no confirmation that the light is produced by the human brain too. But there is confirmation that our axons could transmit the light. So we do have fiber optics capabilities in our brains. It was measured that human body emits biophotons. Based on the detection of light in mammalian brain, and fiber optics in our brain, we could propose hypothesis that [with big probability] our brain also uses biophotons. It’s still to be measured – the biophotonic activity in the human brain.

Even if the light is weak, and fired once per minute, the overall simultaneous enlightenment of the brain is rich for information exchange. It would be huge data bandwidth jump, in comparison to the electrical signals. There is a curious hypo, that specifics of light transmission is what significantly distinguishes the human brain from other mammalian brains. Especially the red shift.

The man without brain introduced many questions. What is the minimum viable brain? Do our brains transmit only electricity, or the big deal in data exchange is carried by light?

When could we confirm the light in the brain? Not soon enough. We banned experiments on cats, to study the mammalian vision & perception. The experiments on the human brain are even more fragile, ethically. Not expecting any breakthrough any time soon…

What we have today is modeling of the cortex layers, as neurons and electrical signals between them, bigger or smaller, depending on the strength of the connections. Functionally, it is modeling of perception. It may look as there is some thinking modeled too, especially in playing games. But wait. In Go game the entire board is visible. In Starcraft the board is not fully visible, and humans recently won from the machines. More difficult than Go is Poker, and Poker winner is Libratus. Libratus is not based on neural nets, it works on counterfactual regret minimization (CFR).

We lack experiments, we lack scanning technologies. We advanced in simulation of perception only, with deep neural nets. Typologies are immature, reusability is low. And those neural nets transmit only abstraction of electricity, not the light.

Learning from Data

Machine Learning is the algorithmic approach, when a program is capable to learn from data. Machine Learning allowed to solve old same problems better. Most popular today is Deep Learning, subset of Machine Learning. To be specific, deep learning allowed to break through in computer vision and speech processing. Today, such routine tasks as image and speech classification/transcription is cheaper and more reliable by machines, than by humans.

Most popular deep learning guys are so called Connectionists. Let’s be honest – there is big hype around deep learning. Many people even don’t know that there are several other approaches to machine learning, besides deep neural nets. Check out the good intro and comparison of machine learning by Pedro Domingos (author of The Master Algorithm). Listen to the fresh stuff from Symbolists Gary Marcus (former Uber) and Francesca Rossi (IBM). Hear fresh Evolutionists stuff from Ilya Sutskever (OpenAI, soon Tesla?) Hear from Analogizers, Maya Gupta (Google). Check out for fresh stuff from Bayesians. Ben Vigoda (Gamalon) on Idea Learning, instead of Deep Learning, Ruslan Salakhutdinov (Apple), Eric Horvitz (Microsoft). Book the date to listen to Zoubin Ghahramani (Uber).

Each machine learning approach gives us a better tool. It is a dawn of the second machine age, with mental tools. Very popular and commercialized niche nowadays. Ironically, all shit data produced by people, converts from useless into useful. All those pictures of cats, food and selfies have become a training data. Even poor corporate powerpoints are becoming training data for visual reasoning. And this aspect of the data metamorphosis is joyful. Obviously this kind of intelligence eats data, and people produce the data to feed it. This human behavior is nothing else as working for the machines, that feels fun. Next time you snap your food or render a creepy pie chart – think that most probably you did it for the machines.

Maybe combination of those approaches could give break through… This is known as a search for the holy grail – master algorithm – for machine learning. To combine or not to combine is a grey area, while the need in more data is clear. Internet of Things could help, by cloning the old good world into its digital representation. By feeding that amount [and high resolution] of data to machines, we could hope, they would learn well from it. But there is no IoT yet, there is Internet and there are no Things. IPv6 was invented specifically for the things, and still not rolled out here or there. Furthermore, learning from data will be restricted by relative shortage of data access. The network bandwidth growth rate is slower than the data growth rate – hence less and less data can make it thru the pipe… Data Gravity will emerge. To access the data, you will have to go to the data, physically, with your tools and yourselves. Data access will be bigger & bigger issue in the years to come. Any better pathway towards creating Intelligence?

Building Complexity

How the intelligence emerged on this planet? It was gradually built, during very long evolution. The diversity and complexity increased in time. We could observe/analyze complex systems emerging over scale and self-organizing over time. Intelligence is a complex system [I think so]. And complex system could do more than only perceive. How? By building/evolving those capabilities. It is very similar to creation of new technology. Everything is possible in this world, just create the technology for that. Technology could be biological, could be digital, whatever. It gives capabilities to do something, that intelligence wants to do. Hence intelligence evolves towards creation of such capabilities. And this repeats and repeats. As result the intelligence grows bigger and bigger.

It worth looking at the place of what we call Artificial Intelligence among other Complex Systems. What I call Intelligence in this post – is what Complex Adaptive Systems do – emergence over scale and self-organization over time. Intelligence could be observed at different levels of abstraction. How 10 trillions molecules emerged and organized to move altogether 1 meter above the ground? How human brain modules or neurons comprehend and memorize? How humanity launch the probe from the Pale Blue Dot outside of the Solar System?

Complexity is not so scary as it looks. There could be no master plan at all, though there could be master config with simple rules. Like the speed of light is this, gravitational constant is that, minimal energy is this, minimal temperature is that and so forth. This is enough to build some enormous and beatiful complexity. Let’s look at the single dimensional primitive rules, and the “universes” they build.

Wolfram Rule 30 will be first. In all of Wolfram’s elementary cellular automata, an infinite one-dimensional array of cellular automaton cells with only two states is considered, with each cell in some initial state. At discrete time intervals, every cell spontaneously changes state based on its current state and the state of its two neighbors. For Rule 30, the rule set which governs the next state of the automaton is: current pattern 111 110 101 100 011 010 001 000, new state for center cell 0 0 0 1 1 1 1 0. Very similar evidence could be observed in nature, on the shell of mollusk.

Wolfram Rule 110. It is an elementary cellular automaton with interesting behavior on the boundary between stability and chaos. Current pattern 111 110 101 100 011 010 001 000, new state for center cell 0 1 1 0 1 1 1 0. Rule 110 is known to be Turing complete. This implies that, in principle, any calculation or computer program can be simulated using this automaton. It is lambda calculus. Hey Python coders, ever coded lambda function? You could compute on the cyclic tag graphs.

Wolfram Rule 110 is similar to Conway’s Game of Life. Also known simply as Life, is a cellular automaton, a zero-player game, meaning that its evolution is determined by its initial state, requiring no further input. One interacts with the Game of Life by creating an initial configuration and observing how it evolves, or, for advanced “players”, by creating patterns with particular properties.

Complexity could be built with simple rules from simple parts. The hidden order will reveal itself at some moment. Actually, the Hidden Order is a work by John Holland, the Evolutionist(?). We need more diverse abstractions, that do/have aggregation, tagging, nonlinearity, flows of resources, diversity, internal models, building blocks – that could become that true Intelligence. Maybe we already built some blocks, e.g. neural nets for perception. Maybe we need to combine growing stuff with quantum approach – probabilities, coherence and entanglement? Maybe energy worth more attention? Learn how to grow complexity. Build complexity. Over scale & time may emerge Intelligence.

PS.

This was my guest lecture for the 1st year students of Lviv Polytechnic National University, Computer Science Institute, AI Systems Faculty. Many of them, all young, open for thinking and doing.

It’s Deep

Today, we are trying Deep Neural Networks on many [previously] unsolved problems. Image and language recognition with CNNs and LSTMs has become a standard. Machines can classify images/speech/text faster, better and much longer than humans.

There is breakthrough in computer vision in real-time, capable to identify objects and object segments. That’s very impressive, it enables self-driving cars, and in-doors positioning without radio beacons or other infrastructure. The machine sees more than human, because the machine sees it all in 360 degrees. And the machine sees more details simultaneously; while human overlooks majority of them.

We created some new kind of intelligence, that is similar to human, but is very different from human. Let’s call this AI as Another Intelligence. The program is able to recognize and identify more than one billion human faces. This is not equivalent what humans are capable to do. How many people could you recognize/remember? Few thousands? Maybe several thousands? Less than ten thousands for sure (it’s the size of small town); so 1,000,000,000 vs. 10,000 is impressive, and definitely is another type of intelligence.

DNNs are loved and applied almost to any problem, even previously solved via different tools. In many cases DNNs outperform the previous tools. DNNs started to be a hammer, and the problems started to be the nails. In my opinion, there is overconfidence in the new tool, and it’s pretty deep. Maybe it slows us down on the way of reverse engineering the common sense, consciousness…

The Man Without Brain

DNNs were inspired by neuroscience, and we were confident that we were digitally recreating the brain. Here is cold shower – a man with a tiny brain – 10% size of the normal human brain. The man was considered normal by his relative and friends. He lived normal life. The issue was discovered accidentally, and it shocked medical professionals and scientists. There are hypothesis how to explain what we don’t understand.

There are other brain-related observations, that threaten the modern theory of brain understanding. Birds – some birds are pretty intelligent. Parrots, with tiny brains, could challenge dolphins, with human-sized brains, and some chimps. Bird’s brain is structured differently from the mammalian brain. Does size matter? Elephants have huge brain, with 3x more neurons than humans. Though the vast majority of those neurons are within different block of the brain, in comparison to humans.

All right, the structure of the brain matters more than the size of the brain. So are we using/modeling correct brain structure with DNNs?

The Structure And The Function

Numenta is working on reverse engineering the neocortex for a decade. Numenta’s machine intelligence technology is built on the own computational theory of the neocortex. It deals with hierarchical temporal memory (HTM), sparse distributed memory (SDM), sparse distributed representations (SDR), self-organizing maps (SOM). The network topologies are different from the mainstream deep perceptrons.

It’s fresh stuff from the scientific paper published in free frontiers magazine, check it out for the missing link between structure and function. “… remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence toward peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities.”

One-Shot Learning

When human is shown a new symbol, from previously unseen alphabet, it is usually enough to recognize the other such symbols, when shown again later. Even in the mix with other known and unknown symbols. When human is shown a new object, for the first time, like segway or hoverboard, it is enough to recognize all other future segways and hoverboards. It is called one-shot learning. You are given only one shot at something new, you understand that it is new for you, you remember it, you recognize it during all future shots. The training set consists of only one sample. One sample. One.

Check out this scientific paper on human concept learning with segway and zarc symbol. DNNs require millions and billions of training samples, while the learning is possible from the only one sample. Do we model our brain differently? Or are we building different intelligence, on the way of reverse-engineering our brain?

God Doesn’t Build In Straight Lines

These are two models of the same kart, created differently. On the left is human-designed model. On the right is machine-designed model within given restrictions and desired parameters (gathered via telemetry from the real kart from the track). It is paradigm shift, from constructed to grown. Many things in nature do grow, they have lifecycle. It’s true for the artificial things too. Grown model tends to be more efficient (lighter, stiffer, even visually more elegant), than constructed ones.

Take a look at those DNNs, including GoogLeNet and MS ResNet. On the left we have human-designed [constructed] models. Imagine what could be machine-generated [grown] on the right…

How to generate? Good start would be to use evolutionary programming, with known primitives for cells and layers. Though it is not easy to get it right. By evolving an imaginable creature, that moves to the left, right, ahead, back, it is easy to get asymmetrical blocks, handling the left and right. Even by running long evolutions, it could be hardly possible to achieve the desired symmetry, observed in the real world, and considered as common sense. E.g. the creature has very similar or identical ears, hands, feet. What to do to fix the evolution? To bring in the domain knowledge. When we know that left and right must be symmetrical, we could enforce this during the evolution.

The takeaway from this section – we are already using three approaches to AI programming simultaneously: domain rules, evolution and deep learning via backpropagation. Altogether. No one of them is not enough for the best possible end result. Actually, we even don’t know what the best result is possible. We are just building a piece of technology, for specific purposes.

The Master Algorithm

The above approach of using domain rules, evolution and deep learning via backpropagation altogether might not be capable to solve the one-shot learning problem. How that kind of problems could be solved? Maybe via Bayesian learning. Here is another paper on Bayesian Framework, that allows to learn something new from few samples. Together with Bayes we have four AI approaches. There is a work on AI, identifying five [tribes] of them.

The essense is in how to learn to learn. Without moving the design of AI to the level when AI learns to learn, we are designing throw-away pieces, like we did with Perl programming, like we do with Excel spreadsheets. Yes, we construct and train the networks, and then throw them away. They are not reusable, even if they are potentially reusable (like substituting the final layers for custom classification). Just observe what people are doing, they all are training from the very beginning. It is the level of learning, not the learning to learn – i.e. it’s throw-away level. People are reusable, they could train again; while networks are not reusable.

The Master Algorithm is the work, that appeals to the AI creators, who are open-minded to try to break through the next level of abstraction. To use multiple AI paradigms, in different combinations. It is design of design – you design how you will design the next AI thing, then apply that design to actually build it. Most probably good AI must be built with special combination of those approaches and tools within each of them. Listen to Pedro Domingos for his story, please. Grasp the AI quintessence.

It was near Central Park in Manhattan, on Avenue of Americas. It was late evening. I was waiting for white lights to cross the street. Noticed a tall man in bright shirt. Recognized him immediately – that man was Peter Norvig – director of research at Google.

I asked for permission to walk with him for a while. We went from 6th to somewhere on 5th avenue, talking about many things. About the man without brain, considered normal by his relatives and friends. About the elephant brain with 3x more neurons than human. About Neural Networks, which are just a new tool for solving specific problems, like differential equations solved other problems earlier; and not replicating/resembling our brain. About The Master Algorithm, and learning to learn, evolutionary algorithms, one-shot learning. About Quantum Computing; especially for Probabilistic Programming, about huge tensor optimization via quantum tunneling. About OpenAI, how Elon Musk and Larry Page hang out in secret apartment in Palo Alto, and what freaked Elon out, to start preventive activity. About Elon-the-businessman, luring/locking professionals to OpenAI and then transferring from OpenAI to Tesla/SpaceX. About the autonomous drones, especially killer drones . About emotions and Affective Computing…

Nowadays Peter is thinking and building a new OS, with natural interface. Think of Her. He still likes the command line interface and bright shirts. With CLI it is obvious – you and machine speak the same language, interactively, it is [almost] natural [for professionals]. With bright shirts – go figure it out yourself…

Start from this cool comparison of Mathematics and Physics by Richard Feynman. Physicists are always about the special case. Mathematicians are always about the general case. Physicists do reverse engineer the world; recreating the technologies, available in the Universe. Physicists even think beyond the Universe…

Continue with these ruminations about Mathematics by Stephen Wolfram – was mathematics invented or discovered. He thinks that the math is already there, we just need to get to those spaces.

Here are details on the Computing Theory of Everything, by Stephen Wolfram. Like Galileo Galilei invented the telescope to observe and discover the far space, Wolfram invented and invents tools to discover the math, all those spaces. It is not combinatoric mess, as the spaces could be shaped nicely, depending on the laws within. Look at this amazing Rule 30, look at this annoying Rule 184.

Think of forthcoming Quantum Computing, which is closer to what Feynman foresaw about machinery without mathematics (watch first video, from 6:00 to 7:30). Why we need an infinite computational power, based on mathematics & logic, to figure out what happens in the tiny place in space? Pretty modern supercomputer needs few hours to simulate 10^11 individual atoms, which is ~10^11 times smaller than the number of atoms in only 1 gram of iron (Fe)…

But about simulating the new worlds, at the level of individual atom. We could build a simulation, and it will go with mathematics. We just need to squeeze the computational power from the physical universe.

Are mathematics and physics converging?

PS. Everything above physics in understood. Chemistry deals at bigger sizes. And so on upwards to huge sizes… till the edge of the Universe, where we still don’t understand. But maybe the Math will help here?

We wanted to know it

Since mankind developped some good intelligence, we [people] immediately started to discover our world. We walked by foot until we could reach. We domesticated big animals – horses – and rode horses to reach even further, horizontally and vertically. So we reached the water. Horses could not bring us across the seas and oceans. We had to create new technology, that could carry people above the water – ships.

Ship building required pretty much calculation itself. And ship only is not sufficient to get there. Some navigation needed. We developped both measurement and calulcation of wood and nails, measurement of time, navigation by stars and sides of the world. That was kind of computing. Not the earliest computing ever, but good enough computing that let us to spread the knowledge and vision of our [flat] world.

Wooden computing

Early device for computing was abacus. Though it is usually called a calculating tool or counting frame, we use word computing, becuse this topic is about computing technology. Abacus as computing technology was designed with size bigger than a man, and smaller than a room. Then the wooden computing technology miniaturized to desktop size. This is important: emerged at the size between 1 and 10 meters, and got smaller in time to fit onto dektop. We could call it manual wooden computing too. Wooden computing technology is still in use nowadays in African countries, China, Russia.

Mechanical computing

Metal computing emerged after wooden. Charles Babbage designed his analytical engine from metal gears, to be more precise – from Leibniz wheel. That animal was bigger than a man, and smaller than a room. Below is a juxtaposition of inventor himself with his creation (on the left). Metal computing technology miniaturized in time, and fit into a hand.

Curt Herzstark made really small mechanical calculator, named it Curta (on the right). Curta also lived long, well into the mid of XX century. Nowadays Curta is favorite collectible, priced at $1,000 minimum on eBay, while majority of price tags are around $10,000 for good working device, built in Lichtenstein.

Electro-mechanical computing

Babbage machine became a gym device, when Konrad Zuse designed first fully automatic electro-mechanical machine Z3. Clock speed was 5-10Hz. Z3 was used to model flatter effect for military aircrafts in Nazi Germany. And first Z3 was destroyed during bombardment. Z3 was bigger than a man, and smaller than a room (left photo). Then electro-mechanical computing miniaturized to desktop size, e.g. Lagomarsino semi-automatic calculating machine (right photo).

Here something new happened – growth beyond the size of a room. Harvard Mark I was big electro-mechanical machine, put in big hall. Mark I served for Manhattan Project. There was a problem, how to detonate atomic bomb. Well known von Neumann computed explosive lens on it. Mark I was funded by IBM, Watson Sr.

So, electro-mechanical computing started from the size bigger than a man, smaller than a room, and then evolved in two directions: miniaturized to desktop size, and grown to small stadium size.

Electrical Vacuum Tube computing

At some point, mechanical parts were redesigned to electrical, and first fully electrical machine was created – ENIAC. It used vaccum tubes. Its size was bigger than a man, smaller than a big room (left photo). The fully electrical computing technology on vacuum tubes got miniaturized to desktop size (right photo).

Very interesting and beautiful was miniaturization. Even vacuum tubes could be small and nice. Furthermore, there were many women in the indutry at the time of electrical vacuum tube computing. Below are famous “ENIAC girls”, with the evidence of miniaturization of modules, from left to right, smaller is better. Side question: why women left programming?

ENIAC was very difficult to program. Here is tutorial how to code the modulo function. There were six programmers who could do it really well. ENIAC was intended for balistic computing. But well known same von Neumann from atomic bomb project, got access to it and ordered first ten programs for hydrogen bomb.

Fully automatic electrical machines grew big, very big, bigger than Mark I, II, III etc. They were used for military purposes, and space programs. IBM SAGE on photo, its size is like mid stadium.

Electrical Transistor computing

First fully transistor machine was build probably by IBM, though there is photo of European [second] machine, called CADET (left photo). There were no vacuum tubes in it anymore. Transistor technology is till alive, very well miniaturized to desktop and hand (right photo).

Miniaturization of transistor computing went even further, than size of the hand. Think of small contact lens, small robots in veins, brain implants, spy devices and so on. And transistors are getting smaller and smaller, today 14nm is not a big deal. There is dozen of silicon foundries capable of doing FinFET at such scale.

Transistor computers grew really big, to the size of the stadium. The Earth is being covered by data centers, sized as multiple stadiums. It’s Titan computer on photo, capable of crunching data at the rate of 10 petaFLOPS. The most powerful supercomputer today is Chinese Sunway TaihuLight at 34 petaFLOPS.

But let me remind the point: electrical transistor computing was designed at the size bigger than a man, smaller than a room, and then evolved into tiny robots, and huge supercomputers.

Quantum computing

Designed at the size bigger than a man, smaller than a room.

Everything is a fridge. The magic happens at the edge of that vertical structure, framed by the doorway, 1 meter above the floor. There is a silicon chip, designed by D:Wave, built by Cypress Semiconductor, cooled to absolute zero temperature (-273C). Superconductivity emerges. Quantum physics start its magic. All you need is to shape your problem to the one that quantum machine could run.

Today, D:Wave solves problems 10,000x faster than transistor machines. There is potential to make it 50,000x faster. Cool times ahead!

Motivation

Why do we need such huge computing capabilities? Who cares? I personally care. Maybe others similar to me, me similar to them. I want to know who we are, what is the world, and what it’s all about.

The Nature does not compute the way we do with transistor machines. As my R&D colleague said about a piece of metal: “You raise the temperature, and solid brick of metal instantly goes liquid. Nature computes it at atomic level, and does it very very fast.” Today one of Chinese supercomputers Tianhe-1A computed behavior of 110 billion atoms during 500,000 evolutions… Is it much? It was only 0.1 nanosecond corresponding to real time, done in three hours of computing.

Let’s do another comparison for same number of atoms. It was about 10^11 atoms. If it was computed at the rate of 1 millisecond, then it would be only 500 seconds, less than 10 minutes. My body has 10 trillions molecules, or about 10^28 atoms. Hence, to simulate entire me during 10 minutes at the level of individual atoms, we would need 10^18x more Tianhe-1A supercomputers… Obviously our current computing is wrong way of computing. Need to invent further. But to invent further, we have to adopt new way of computing – quantum computing.

Who needs such simulations? Here is counter question – what is Intelligence? Intelligence is our capability to predict the future (Michio Kaku). We could compute the future at atomic level and know it for sure. The stronger intelligence is, the more detailed and precise our vision into the future is. As we know the past, and know the future, the understanding of time changes. With really powerful computing, we know for sure what will be in the future as accurately as we know what happened in the past. Distant future is more complicated to compute as distance past. But it is possible, and this is what Intelligence does. It uses computing to know the time. And move in time. In both directions.

Conclusion

All computing technologies together, on one graph, show some pattern. Horizontaly we have time, from past (left) to future (right). Vertically we have scale of sizes, logarithmic, in meters. Red dot shows quantum computing. It is designed already, bigger than a man, smaller than a room. Upper limits are projected bigger than modern transistor supercomputers. Lower is unknown. It’s OK that both transistor and quantum computing technologies coexist and complement each other for a while.

All right, take a look at those charts, imagine quantum lines continuation, what do you see? It is Software is eating the World. Dragon’s tail is on the left, body is in the middle, and the huge mouth is on the right. And this Software Dragon is eating ourselves at all scales. Somebody calls it Digitization.

Software is eating the World, guys. And it’s OK. Right now we could do 10,000x faster computing on quantum machines. Soon we’ll be able to do 50,000x faster. Intelligence is evolving – our ability to see the future and the past. Our pathway to time machine.

Saving Power

Keeping 97 percent of the market is very lucrative. But it is also fragile, because nothing lasts forever. Intel keeps 97 (or even 98) percent of the server processor market. It was result of brilliant strategy some time ago, when Intel decided to adopt power saving strategy, while AMD was pursuing the clock speed. So we got plenty of Intels, Intel here, Intel there, Intel everywhere (like in So What song). Saving power was good strategy. AMD is off the server processor market.

Growing Phones

During that time, outside of datacenters, smart phones and other hand held gadgets became mainstream as consumer devices. They were running on other [simpler] chips, like ARM and ARM derivatives. Multiple companies produced them. Everybody could license ARM design, add own stuff, customize it all and make own chip.

About 10 years ago, we got smartphone, equal by computing power to Apollo mission. Today our smartphones are computers that could phone, rather than phones that could compute. Mobile processors grew bigger and bigger, co-processors emerged. As result we got pretty equivalent of personal computer in small factor.

Intending to Eat the Datacenter

Why not using fat mobile processors in the datacenter, instead of more complicated and power greedy Intel ones? That was logical and somebody started to look there. Not for general use, but for high-performance computing, where GPU is not suitable (because of too small cores and memory copying inconveniences). Also for storage, especially cold storage.

Calxeda made big waves some time ago, in 2011. They designed really small servers named EnergyCore, which could be tightly packed into the 1U or 2U rack. After failing to sign a deal with HP, funding was cut, and Calxeda shut down. That sucks, because there was a need on the market (OK, there was at least logical evidence). We could have 480-core server, consisting of 120 quad-core ARM Cortex-A9 CPUs if it all didn’t flop. Most probably their processor was not jucy enough, hence declined by HP.

Eating the Datacenter

Others tried too at the same time. AppliedMicro announced X-Gene chip also back in 2011. The roadmap is long. Today we have X-Gene2, and powerful X-Gene3 which could battle Xeon E5 is scheduled to second half of 2017. Slowly but reliably it has started, ARM started to eat the datacenter with 64-bit ARMv8. Same performance at lower power consumption, and in significantly smaller factor [of the entire server].

What is coolest is SoC. All those ARMs are actually CPU plus infrastructure like memory channels, slots for disks, networking. It allowes to reduce the size of the entire server board dramatically. 1/3 of 1U rack could contain 6 ARM SoCs, each of 50-70 cores, which is equal to 300-400 processors per sled. Or almost 1,000 processors in 1U. Each core with good clock speed at 2.5GHz or so. With 2-3x less power consumption than Xeons. HP are building experimental ARM servers, not on Calxeda chips, but on AppliedMicro, check out Moonshot.

Cavium produced chips for network and storage appliances, and suddenly released jucy chip ThunderX, and jaws dropped. It was 48-core 64-bit ARMv8, with 2.5GHz clock speed. One of the biggest datacenters in the world – OVH – is running on ThunderX already. Recently Cavium redesigned it completely to ThunderX2. 54-core SoC, for high-density racks, not bad at all.

What Intel Does?

Intel builds Xeon Phi. They started from co-processor and moving to host/bootable processor, named Knights Landing. Still to be released. It should have ~260 cores, each core as small Pentium. So compatibility with Wintel era must be retained. For good or for bad? Compatibility was always burden, but it was always needed by the market. How to continue to run all those apps? SAP or Oracle or Windows may not run well on ARM today.

Intel produced less power greedy Xeon-D, especially for Facebook, Microsoft and Google needs. But it is really interesting what than Knights Landing aka KNL will be. There were some screenshots of the green screen and motherboard available. Premium equipment makers Penguin Computing announced both ThunderX and Xeon Phi support in their highly dense sleds. Check out ThunderX and Knights Landing sleds.

What should Intel do? They definitely have big plans, because spending ~$17B on Altera was well thought. Though is FPGA & IoT strategy well aligned with keeping datacenter hegemony? Good ruminations are assembled in the post by Cringely.

Aha Moment

Without Qualcomm it is difficult to tell how it all will unfold. Some companies tried and flopped, like Calxeda, and $130 millions did not help. Some unusual players came in, like Cavium, and made noticable waves. AppliedMicro decided to build own processor. Amazon bought Annapurna to build own processor for AWS cloud (for ~$370 millions). There is some uncertainty still, what Amazon already made from that acquisition.

Qualcomm made some non-technical announcements, but still have to deliver the product. From that point, ARM eating the datacenter could accelerate and go mainstream. So waiting for aha moment. It must happen by mid 2017 or sooner. It is going to be at 10nm. And it is thrilling – what comes from Qualcomm?

Who got the POWER?

I did not address POWER8 and POWER9 here, because nobody makes them except IBM themselves (though select semiconductors say on their sites they do power processors). Google experimented with POWER, RackSpace experimented with POWER. But RackSpace delayed Barreleye servers. And Google also experimented with ARMs, and were not so excited. Perhaps because that test chip was quite big and had only 24 cores.

It all points towards the ARM as new general purpose, HPC (where GPU is not applicable) and storage servers. And it all points to Qualcomm, they will be a cornerstone of datacenter revolution.

How AI tools can be combined with the latest Big Data concepts to increase people productivity and build more human-like interactions with end users. The Second Machine Age is coming. We’re now building thinking tools and machines to help us with mental tasks, in the same way that mechanical robots already help us with physical work. Older technologies are being combined with newly-created smart ones to meet the demands of the emerging experience economy. We are now in-between two computing ages: the older, transactional computing era and a new cognitive one.

In this new world, Big Data is a must-have resource for any cutting-edge enterprise project. And this Big Data serves as an excellent resource for building intelligence of all kinds: artificial smartness, intelligence as a service, emotional intelligence, invisible interfaces, and attempts at true general AI. However, often with new projects you have no data to begin with. So the challenge is, how do you acquire or produce data? During this session, Vasyl will discuss what the process of creation of new technology to solve business problems, and the strategies for approaching the “No Data Challenge”, including:

Using software and hardware agents capable of recording new types of data;

The Five Sources of Big Data;

The Six Graphs of Big Data as strategies for modern solutions; and

The Eight Exponential Technologies.

This new era of computing is all about the end user or professional user, and these new AI tools will help to improve their lifestyle and solve their problems.

Pterodactyl

…Then the Pterodactyl burst upon the world in all his impressive solemnity and grandeur, and all Nature recognized that the Cainozoic threshold was crossed and a new Period open for business, a new stage begun in the preparation of the globe for man. It may be that the Pterodactyl thought the thirty million years had been intended as a preparation for himself, for there was nothing too foolish for a Pterodactyl to imagine, but he was in error, the preparation was for Man… — Mark Twain

Lance Armstrong

The Man. The man who won Tour de France seven times. Having reached the human limit of physical capabilities, he [and others] extended them. He did blood doping (by taking EPO and other drugs, storing own blood in the fridge, and infusing it before the competition for boosting the number of red blood cells, thus performance). He [and others] took anti asthmatic drugs to increases performance on endurance. And so on, so on. There are Yes or No answers from Lance himself from Oprah’s interview.

Is Lance cheater? Or is Lance hero? I consider him a hero for two reasons. First, he competed against the same or similar. Second, he went beyond the human limits, cutting-edge thinking, cutting-edge behavior, scientific sacrifice, calculated or even bold risk.

What could be said about all other sportsmen? I think the sporting pharmacology is evolutionary logical stage for the humankind to outperform our ancestors, to break the records, to win, and continue winning. If sportsmen are specialized in competing, and society wants them competing, then everything all set. Evolution goes on, biological meets artificial chemical. It improves the function, it solves the problem. Though it slightly distance biological ourselves from what we though we were.

Prosthetics

It happens that people lose body parts. It is right way to go to give them missing parts. It’s still very complicated, the technologies involved are still not there, but good progress has been made. There are new materials, new mechanics, new production (digital manufacturing, 3D printing), new bio-signal processing (complex myogram readings), new software designed (with AI), and all together it gives tangible result. Take a look at this robot, integrated with the man:

Some ethical questions emerge. The man with prosthetic body part is still a biological being? What is a threshold between biological parts and synthetic parts to be considered a human being? There are people without arms and legs, because of injuries or because of genetic diseases, like Torso Man. We could and should re-create the missing parts and continue living as before, using our new parts. Bionic parts must evolve until they feel and perform identically to original biological parts.

It relates to invisible organs too. The heart, which happen to be a pump, not a soul keeper. People live with artificial hearts. Look at the man walking out from hospital without human heart. The kidneys, which are served by external hemodialysis machines. New research is performed to embed kidney robots into the body. Ethical questions continue, where is a boundary what we call a ‘human’? Is it head? Or brain only? What makes us human to other humans?

Genetics

We are defined by our genes. Our biological capabilities are on genes. Then we learn and train to build on top of our given foundation. We are different by genes, hence something that is easy for one could be difficult for another. E.g. since childhood sportsmen usually have better metabolism in comparison to those who grow to ‘office plankton’.

There are diseases caused by harmful mutations on genes. Actually any mutation is bad, because of unpredictable results in first generation with new mutant [gene]. But some mutations are bad from generation to generation, called genetic disease. It is possible to track many diseases down to the genes. There are Genetic Browsers allowing to look into the genome down to the DNA chain. Take a look at the CFTR gene, first snapshot is high-level view, with chromosome number and position; second is zoomed to the base, with ACGT chain visible.

If parents with genetic disease want to prevent their child from that disease, they may want to fix the known gene. Everything else [genetically] will remain naturally biological, but only that one mutant will be fixed to the normal. The kid will not have the disease of ancestors, which is good. A question emerges: is this kid fully biological? How that genetic engineering impacts established social norms?

What if parents are fans of Lance Armstrong and decide to edit more genes, to make their future kid a good sportsman?

What is Life?

Digging down to the DNA level, it is very interesting to figure out what is possible there to improve ourselves, and what is life at all. How to recognize life? How would we recognize life on Mars, if it’s present there?

Here is definition from Wikipedia: “The definition of life is controversial. The current definition is that organisms maintain homeostasis, are composed of cells, undergo metabolism, can grow, adapt to their environment, respond to stimuli, and reproduce.” The very first sentence resonate with questions we are asking…

Craig Venter led the team of scientists to extract the genetic material from the cell (Mycoplasma genitalium), instrumented its genome by inserting the names of 20 scientists and the link to the web site, implanted edited material back into the cell, observed the cell reproducing many times. Their result – Mycoplasma laboratorium – reproduced billions times, passing encoded info through generations. The cell had ~470 genes.

What is absolutely minimum number of genes, and what are those genes, to create life? Is it 150? Or less? And which one exactly? What are their specialization/functions? It’s current on-going experiment… Good luck guys! Looking forward to your research success, and what is Minimum Viable Life (MVL). BTW by doing this experiment, scientists designed new technologies and tools, allowing to model the genes programmatically, and then synthesize them at molecular level.

Here Come the Robots

While somebody are digging into the genome, others are trying to replicate humans (and other creatures) at macro level. Most successful with humanoid machines are Boston Dynamics.

How far we are to make them indistinguishable from humans? Seems that pretty far. The weight, the gravity center, motion, gestures, look & feel are still not there. I bet that humanoids will be first create in military and porn. Military will need robots to operate plenty of outdated military equipment, serve and combat in hazard environments. it’s only old weaponry that require manned control. While new weapons are designed to operate unmanned. Porn will evolve to the level that we will fuck the robots. For military it’s more the economical need. For our leisure it’s romantic need and personal experience.

The size and shape of robots doing mechanical work is so different. From tunnel drilling monsters to blood vessels…

All 8 Together

If we look for the commonality in mentioned (and several unmentioned) disrupting technologies, we could select 8 of them (extended and reworked 8 directions of Singularity Univeristy), which stand out:

Biology and Biotech

Medicine and Longevity

Robotics

Network and Sensors

Fabrication and 3D Printing

Nanotech and Materials

Computing

Artificial Intelligence

As we slightly covered Biology, Medicine and Robotics already, more to be said about the rest. But before than, few words about Biotech. We could program new behavior of the biomass, by engineering what the cells must produce, and use those biorobots to clean the landfills around the cities, sewerage, rivers, seas, maybe air. Biorobots also could clean our organisms, inside and outside. Specially engineered micro biorobots could eat the Mars stones and produce the atmosphere there. Not so fast but feasible.

Well, more words about other disrupting technologies. Networks and Sensors next. First of all – it’s about networks between human & human, machine & machine, human & machine. The network effect happens within the network, known as Metcalfe’s Law. Networks are wired and wireless, synchronous and asynchronous, local and geographically distributed, static and dynamic mesh etc. Very promising are Mesh Networks, allowing to avoid Thing-Cloud aka Client-Server architectures, despite all cloud providers pushes for that. Architecturally (and common sense) it’s better to establish the mesh locally, with redundancy and specialization of nodes, and relay the data between the mesh and the cloud via some edge device, which could be dynamically selected.

Sensors will be everywhere. Within interior, on the body, as infrastructure of the streets, in ambient environment, in the food etc. Our life is improved when we sense/measure and proactively prepare. We used to weather forecasts, which are very precise for a day or two. It’s because of huge amount of land sensors, air sensors, satellite imagery. Body sensors are gaining popularity, as wearables for quantified self. There are recommendations for the lifestyle, based of your body readings. It’s early and primitive today, but it will dramatically improve with more data recorded and analyzed. Modern transportation requires more sensors within/along the roads and streets, and cars. It’s evolving. Miniaturization shapes them all. Those sensors must be invisible for the eyes, and fully integrated into the cloths and machines and environment.

3D Printing. The biggest change is related to ownership of intellectual property. 3D model will be the thing, while its replication at any location on demand on any printer will be commodity function. Many things became digital: books, photos, movies, games. Many things are becoming digital: hard goods, food, organs, genome. It’s a matter of time when we have cheap technology capable to synthesize at the atom grid level and molecular. New materials are needed everywhere, especially for human augmentation, for energy storing and for computing.

Nanotech. We learn to engineer at the scale of 10^-9 meter. From non-stick cookware and self restoring paint (for cars), to sunscreen and nanorobots for cleaning our veins, to new computing chips. Nano & Bio are very related, as purification and cleanup processes for industry and environment are being redesigned at nano level. Nano & 3D Printing are related too, as ultimate result will be affordable nanofactory for everyone.

Computing. We’re approaching disruption here, Moore’s Law is still there but it’s slowing down and the end is visible. Some breakthrough required. Hegemony of Intel is being challenged by IBM with POWER8 (and obviously almost ready POWER9) and ARM (v8 chips). Google is experimenting with POWER and ARM. it’s true, Qualcomm is pushing with ARM-based servers. D:Wave is pioneering Quantum Computing (actually it’s superconductivity computing). There is good intro in my Quantum Hello World post. IBM recently opened access to own quantum analog. The bottom line is that we need more computing capacity, it must be elastic, and we want it cheaper.

Artificial Intelligence. AI deserves separate chapter. Here it is.

Artificial Intelligence

The purpose of AI was machine making decisions ( as maximization of reward function). But being better at making decisions != making better decisions. Machine decide how to translate English-to-Ukrainian, but not speaking either language. Those programs (and machines) are super screwdrivers, they don’t what to do, we want them to do, we put our want into them.

AI is different intelligence, human cannot recognize 1 billion humans, even really having seen them all many times. AI is Another Intelligence so far. The shape of thinking machines is not human at all: DeepBlue – chess winner – is a toll black box; Watson – Jeopardy winner – 2 units of 5 racks of 10 POWER7 servers between noisy refrigerators in nice alien blue light (watch from 2:20); Facebook Faces – programs and machines recognizing billions of human faces – it’s probably big racks in data center, Google Images – describing context of the image – big part of the data center (detection of cat took 16,000 servers several years ago); Space Probes – totally different from both humans and black toll boxes in the data centers.

BTW if somebody really spots UFO visiting our planet, don’t expect green men, as organics is poor for space travel, because of dangerous +200/-200 Celsius temperature range, ultra violet and radiation, time needed for travel (even through the wormhole)… That UFO is a robot most probably. Or intelligence on non-biological carrier, which means post-biological species (which is worse for us if so).

Our wet brain operates at 100 Watts, while the copy of the simulation of the same number of cells requires 10^12 Watts. Where on Earth will we get 1 trillion watts just for equivalent of one human intelligence? Even not intelligence, but connectivity of the neurons. Isn’t it ridiculous pseudo architecture? We still did not isolate what we call consciousness, and we don’t know it’s structure to properly model it. Brain scanning is in progress, especially for deeper brain. And this Eureka moment, like we got with DNA, is still to come.

We’re remaining at the center, creating and using machines for mental work, like we created and used/use machines for physical work. Humans with new mental tools should perform better than without them. Google is a typical memory machine, and memory prosthesis. Watson as a layer or a doctor is a reality.

Back from the future, at present we have intelligent machines – governments and corporations. We created those artificial bodies many years ago, and just don’t realize they are true intelligent machines. They are integrated into/with society, with law evolved through precedents and legislation, tailored to different locations and cultures. Culture itself is a natural artificial intelligence. Global biological artificial intelligence emerged on politicians, lawyers, organizations like United Nations and hundreds of smaller international ones. They are all candidates for substitution by programs and machines.

Interesting observation is that most intelligent humans neither harmful nor rulers of others. Hence we could assume that really smart AI will not be harmful to humans, when AI will be approximately at our level. But it’s uncertain about accelerated and grown AI later in time. Evolution will shape AI too, continuing from invisible interfaces with machines right now. We could stop clicking, typing, tapping into machines, and talk to them like we do between ourselves. Today we have three streams of AI: < 3yo AI, Artificial Smartness, Intelligence as a Service.

We are what we eat, hence they will have to eat us? Hm… Real AI will not reveal itself. And most probably they will leave, like we left our cradle Africa…

Exponential Today

There were some concerns that we had slowed down, by observations and perception of the daily facts. But it’s also visible that several technologies are booming and disrupting our lives almost on weekly basis. Those 8 mentioned earlier technologies in section It All Together. Those technologies are developing exponentially.

The companies are highly specializing within their niches, performing at global scale. Global economy is changing. Few best providers of the narrow function do it world-wide. E.g. Google is serving search globally, with two others far behind (Baidu and Bing, with artificial restriction of Google in China). Illumina chips are used for gene sequencing (90 percent of DNA data produced). Intel chips are primary host processors in the servers. Nvidia are primary coprocessors and so on. Few companies fulfill the 95+ percent of the needs within some niche. Where this has not happen yet, big disruption is expected soon.

This is pure specialization of work at global scale. Shift from normal distribution to power distribution. Some may say that it’s path to global monopolism, with artificially hold high costs. But in fact it is not, as Google search is free. Illumina is promising full human genome sequenced under $1,000. And Intel still ships new chips according to Moore’s Law, 2x productivity per $1 every 1.5 year.

As global specialization reduces global costs, because same functions and products are produced more efficiently on same resources, it is good for our planet, with limited resources. But here another thing happens, we are not preserving resources, we are using them for creating new technologies, which are expensive, unique, disrupting. Provider of such new technology (and product, service) is not a monopolist, because of small scale/capacity at the beginning. Either they scale or others replicate it, and true leader emerges and make it globally. Also new ways for energy are found, from Sun and wind, and new nuclear too. We’re creating more wealth.

Digitization

Scaling globally is dramatically easier and cheaper for digital products and services, than for physical/hard or hybrid. It is main motivator for digitization of everything. Software is eating the world, because it is simply cheaper to deliver sw vs. hw. Everything will become software, except the hardware to run the software, and power plants to empower the hardware.

Real life is becoming digital very fast. Why we’re taking photos of our meals and rooms, self faces and legs, beautiful and creepy landscapes, compositions? Why we checkin, express status, emotions for others’ expressed statuses, commenting, trolling and even fighting digitally? We also voting, declaring, reporting, learning, curing, buying and consuming, entertaining digitally too. We’re living digitally more than physically sometimes. Notice how people record the event looking at their smartphone small screen instead of looking at the big stage and experience it better. Some motivation drives us to record it to multiple phones, from multiple locations, aspects, angles, distances, and push it into the internet, and share with others. Then see it all from those recordings, own and theirs. Why is it happening? Why we are shifting to digital over natural? Or digital is new natural, as evolution goes on?

Kit Harington was stopped by cop for speeding. The cop made ultimatum – either driver pays fine, or he tells whether Jon Snow is alive in next season. The driver avoided the speeding ticket by telling the virtual/digital story to the cop. For the cop digital virtual was more important than physical biological. Isn’t it natural shift to new better reality?

Many people live is virtual worlds today. Take American and ask about ISIS. Take Syrian and ask about ISIS. Take Ukrainian and ask about Crimea and Donbass. Take Russian and ask about Crimea and Donbass. Same for Israel and Palestina. People will tell opposite everything. People are already living in virtual worlds, created by digital television and internet. Digitization of life is here already, and we are there already.

One

Specialization is observed at all levels. Molecules specialized into water, gases, salts, acids. Bigger molecules specialized into proteins and DNA. Then we have cells, stem cell and their specialization into connective tissue, soft tissue, bone and so on. Next are organs. Then body parts. Specialization is present at each abstraction level. At the level of people specialization is known as roles and professions. Between businesses and countries it is industries. Between nations it is economics and politics.

It looks like we are part of the bigger machine, which is evolving with acceleration. We are like cells, good and bad, specialized from vision to thinking. Roads, pipes are like transportation systems for other cells and payload. Internet (copper and fiber) is more like a neural system. Connectivity is a true phenomenon. We are now fully disconnected (and useless) without smartphone, or without digital social network in any form. Kevin Kelly once called it the One. The Earth of many people will evolve into earth of augmented people and machines, they all specialize and unite into the One.

And since the One, it all looks like just a beginning. I feel another One, and more cells-ones, organizing something more complex and intelligent from themselves. If our cells could specialize and unite into 10 trillions and walk, think, write, why it can’t be possible with bigger cells like One, at bigger scale like Galaxy?

The Man is not the last smart species on Earth. In other words, there will be a day, when the Last [current] Man on Earth goes extinct. What will happen faster: transhuman or true AI, that could replicate and grow? I bet on transhuman. Better for humanity too. For now.